摘要
为准确分选中药材原料中的杂质与存在缺陷的物料,针对微小缺陷物料难检测等问题,提出了一种改进YOLOv6的中药材分选算法。首先,基于药材特点,裁剪检测网络冗余的大目标检测头,减少误检;其次,引入SPD卷积替代跨步卷积进行图像下采样,增强细粒度特征提取能力;最后,引入协同注意力机制,提高重要特征的关注度。在黄芪数据集进行实验。结果表明,改进的YOLOv6算法mAP达到了86.2%,比原算法提升了2.9%,对存在微小缺陷的药材检测能力更强。
In order to accurately sort the impurities and defective materials in the raw Chinese medicinal materials,an improved YOLOv6 algorithm for Chinese medicinal materials sorting is proposed to solve the difficult detection of the materials with inconspicuous defective feature.First,based on the characteristics of medicinal materials,the redundant large target detection head of the detection network is cut to reduce false detections;Then,the SPD convolution is introduced instead of strided convolution for image downsampling to enhance the ability to extract fine grained features.Finally,the coordinate attention mechanism is introduced in the network to improve the attention of important features.The experiments are conducted based on the astragalus dataset.The results show that the mAP of the improved YOLOv6 algorithm is 86.2%,which is 2.9%higher than the original algorithm and has the stronger detection ability for small defective medicinal materials.
作者
李文杰
项安
LI Wen-jie;XIANG An(College of Electronic and Information Engineering,Tongji University,Shanghai 201804,China)
出处
《电脑与信息技术》
2023年第6期15-18,共4页
Computer and Information Technology
关键词
中药材分选
YOLOv6
SPD卷积
注意力机制
Chinese medicinal materials sorting
YOLOv6
SPD convolution
attention mechanism
